Bone marrow hypermetabolism on 18F-FDG PET as a survival prognostic factor in non-small cell lung cancer.
Bibliographic record
Abstract
UNLABELLED: PET is now widely used in the diagnosis and staging of lung cancer with (18)F-FDG. The purpose of the study was to evaluate the prognostic value of diffuse bone marrow hypermetabolism along with other PET prognostic factors with respect to survival and compare them with other established prognostic factors in a large cohort of patients. METHODS: Of 255 patients referred for evaluation of a suspicious lung lesion by PET over an 8-mo period (May 1999 to January 2000), the outcome of 120 patients with a final diagnosis of primary non-small cell lung cancer was analyzed retrospectively after excluding subjects with benign, metastatic, or recurrent lesions, using the available follow-up information and a provincial mortality database. Kaplan-Meier survival curves were compared using the mean and the maximal tumor standardized uptake value (SUV), bone marrow SUV, PET stage, various laboratory parameters, sex, age, conventional imaging stage, and pathologic stage. A stepwise Cox proportional hazard model was built using the significant variables on univariate analysis. RESULTS: The primary tumor SUV (>10), bone marrow uptake of (18)F-FDG, (18)F-FDG PET stage, pathologic stage, hypercalcemia, lactate dehydrogenase, hemoglobin, albumin, thrombocytopenia, thrombocytosis, and leukocytosis were predictors of mortality on univariate analysis. On multivariate analysis, bone marrow hypermetabolism, (18)F-FDG PET nodal stage, and some hematologic parameters (hemoglobin, platelets, white blood cell counts) remained significant independent predictors of mortality. CONCLUSION: Bone marrow hypermetabolism and the PET nodal stage were strong independent predictors of mortality in patients with lung cancer. The primary tumor SUV, though predictive on univariate analysis, was not an independent predictor of mortality in our model.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".